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Written Edition English Tutorial

Machine Learning with Python: From Basics to Capstone

Curriculum

12 Sections
49 Lessons
10 Weeks

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Module 1: Introduction to Machine Learning
Give students a clear foundation in what Machine Learning is, how it differs from related fields, and where it’s applied in real life.
6
Module 2: Python Essentials for ML
5
Module 3: Data Wrangling & Visualization, starting with:
5
Module 4: Data Preprocessing
5
Module 5: Regression Algorithms!
This module introduces the foundational concept of regression in machine learning — perfect for prediction tasks like house prices, salaries, etc.
5
Module 6: Classification Algorithms
5
Module 7: Decision Trees
6
Module 8: Model Evaluation & Tuning
5
Module 9: Unsupervised Learning
5
Module 10: Dimensionality Reduction
5
Module 11: Capstone Project Preparation
5
Module 12: Capstone Project Execution
5

Project Guidelines and Dataset Selection – Capstone Preparation

📂 Project Guidelines & Dataset Selection

Learn how to choose a project scope, select a dataset, and follow professional data ethics for your capstone project.

✅ Project Guidelines

Pick a problem relevant to real-world needs
Keep scope realistic (doable in 2–4 weeks)
Document every step for portfolio purposes
Maintain code readability and version control (Git)

📊 Dataset Sources

Kaggle (Finance, Healthcare, Retail, etc.)
UCI Machine Learning Repository
Government open data portals
APIs (Twitter, OpenWeather, etc.)

Self-check: Why is it important to choose a dataset with clear and consistent labeling for a supervised learning project?

Dimensionality Reduction & PCA in Machine Learning – Easy Guide

Define Problem Statement and Goals – ML Capstone Prep

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